A new generation of microscope and fluorescent probe technologies has enabled highly sensitive quantitative characterization of subcellular objects, such as discrete proteins, organelles, receptors, vesicles, axon terminals and dendritic spines, in live cell digital microscopy based functional assays. The multi-dimensional images in these assays contain huge information content; hundreds or thousands of subcellular objects, or puncta, per image frame and hundreds or thousands of frames per image. The manual recognition of objects in these images is too time consuming. The introduction of automated recognition methods can improve the analysis speed and statistical power of the assay in an unbiased fashion. However, when approaching the sensitivity limit, the fluorescently labeled puncta often exhibit weak signal that is unstable due to noise and variations. This can confound traditional methods of image recognition, and imposes a critical limitation on the achievable resolution, sensitivity and characterization accuracy of the assay. New technologies are needed to enable the robust and accurate analysis of puncta related phenotypes in microscopy images; these technologies enable analytic tools that can reliably screen for puncta related phenotypes at high rates in both basic research (e.g. phenotyping) and applied research (e.g. drug discovery).
Innovative informatics approaches that enable and improve live cell, time-lapse studies of subcellular components will be a fundamental technology underlying critical, next generation assays of cellular function. Subcellular assays give scientists greater resolution as they attempt to dissect cell function, because they yield more direct measurements of the subcellular components underlying the cell functions. For example, measuring the release of a single synaptic vesicle, rather than the neurotransmitter release of the entire cell, or measuring the receptors in a single dendritic spine, rather than those of the whole cell.
The outcome of a quantitative time-lapse assay is an estimate of the parameters of the underlying model such as the “the law of mass action” (Elwenspoek M, “The ideal law of mass action and first-order phase transition”, J. Phys. A: Math. Gen. 16 No 12 (21 Aug. 1983) L435-L437). This is accomplished by fitting measured data to the model. The fitting process makes an assumption of the scatter of the measured data and performs maximum likelihood estimation or other estimation methods.
Conventional methods of model fitting are sensitive to noise or distortion in the measured data. Current High Content Screening tools: Cellomics ArrayScan' assay analysis. (Dunlay et al. 1999. System for cell-based screening. U.S. Pat. No. 5,989,835.), Cellomics KineticScan product, (Sammak, Paul et al. 2003. Automated assay approach for identification of individual cells during kinetic assays. US patent application 20030036853.), GE's In Cell Analyzer high content screening product (Harris, Timothy D. et al. 2003. Method and apparatus for screening chemical compounds. US Patent Application no. 2003/0036855.) and spot analysis in receptor internalization assays (Rubin, Richard et al. 2004. System for cell-based screening. US patent application 20040101912.); lack robust, integrated model fitting capability and only offer assay statistics such peak intensity, time to peak intensity, average intensity (over the time course), number of oscillations etc.